Results 41 to 50 of about 133,258 (310)
Inferring Causal Explanations [PDF]
, 1999 A popular approach to explanations amounts to backward chaining over logical implications encoding causal links. However, the resulting explanations are often unsatisfactory from a common-sense point of view. We define a framework allowing us to distinguish causal implication from mere logical implication.
Philippe Besnard, Marie-Odile Cordier
openaire +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Combined oral contraceptive (COC) use in obese adult women dramatically increases the relative risk of developing a pulmonary embolism (PE). The risk of a PE in obese adolescent females taking contraceptives is currently unknown. The purpose of this investigation was to determine the effect of body mass index (BMI) and contraceptive
John Puetz, Joanne Salas
wiley +1 more source
y0-causal-inference/y0: v0.2.6 [PDF]
<h2>What's Changed</h2> <ul> <li>Improve variable sort and CF graph testing by @cthoyt in https://github.com/y0-causal-inference/y0/pull/199</li> <li>Use frozensets for interventions by @cthoyt in https://github.com/y0-
Jeremy Zucker +4 more
core +1 more source
Improving Outcomes in Machine Learning and Data-Driven Learning Systems using Structural Causal Models [PDF]
, 2023 The field of causal inference has experienced rapid growth and development in recent years. Its significance in addressing a diverse array of problems and its relevance across various research and application domains are increasingly being acknowledged ...
Mbogu, Henry Maduka
core
Reward or punishment? The distribution of life-cycle returns to political office
Political Science Research and MethodsThe remuneration of MPs affects who engages in politics. Even if average returns to office are positive, as found in all other studies, some officeholders’ returns are likely negative.
Jens Olav Dahlgaard +2 more
doaj +1 more source
Journal of Causal Inference, 2014 A philosopher, a medical doctor, and a statistician talk about causality. They discuss the relationships between causality, chance, and statistics, resorting to examples from medicine to develop their arguments.
Chambaz Antoine +2 more
doaj +1 more source
Data integration in causal inference
WIREs Computational Statistics, 2022 AbstractIntegrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This article reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations.
Xu Shi, Ziyang Pan, Miao Wang
openaire +5 more sources
y0-causal-inference/y0: v0.2.7 [PDF]
<h2>What's Changed</h2> <ul> <li>Add SCM parameter estimation by @cthoyt in https://github.com/y0-causal-inference/y0/pull/201</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/y0 ...
Jeremy Zucker +4 more
core +1 more source
环境与职业医学In environmental epidemiological research, extensive non-random environmental exposures and complex confounding biases pose significant challenges when attempting causal inference.
Hui SHI +6 more
doaj +1 more source
Multisensory Causal Inference in the Brain [PDF]
PLOS Biology, 2015 At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object,
Kayser, Christoph, Shams, L.
openaire +7 more sources